Autonomous Materials Discovery for Organic Photovoltaics
Abstract
We aim to develop an AI-guided autonomous materials design approach to discover high-performance organic photovoltaics (OPVs). Autonomous synthesis, automated characterization, and AI-based methods will be integrated into a closed-loop approach to drive molecular discovery guided by target criteria for OPV performance: efficiency and stability. The long-term goal of the project is two-fold: (1)in terms of fundamental science, we aim to fill key knowledge gaps in understanding how molecular structure determines OPV stability and efficiency, and advance the science of closed-loop autonomous discovery by learning how to synergistically integrate AI, automated synthesis, and automated testing. (2)In terms of technology, we aim to meet the “10-10” target (10\% efficiency and 10-year stability for OPV materials) to make OPVs a commercial reality for next-generation energy capture applications and for mitigating climate change.
Cite
Text
Hwang et al. "Autonomous Materials Discovery for Organic Photovoltaics." NeurIPS 2022 Workshops: AI4Mat, 2022.Markdown
[Hwang et al. "Autonomous Materials Discovery for Organic Photovoltaics." NeurIPS 2022 Workshops: AI4Mat, 2022.](https://mlanthology.org/neuripsw/2022/hwang2022neuripsw-autonomous/)BibTeX
@inproceedings{hwang2022neuripsw-autonomous,
title = {{Autonomous Materials Discovery for Organic Photovoltaics}},
author = {Hwang, Changhyun and Yi, Seungjoo and Friday, David and Angello, Nicholas Henry and Torres-Flores, Tiara Charis and Jackson, Nick and Burke, Martin D. and Schroeder, Charles and Diao, Ying},
booktitle = {NeurIPS 2022 Workshops: AI4Mat},
year = {2022},
url = {https://mlanthology.org/neuripsw/2022/hwang2022neuripsw-autonomous/}
}